453 citations as recorded by crossref.

1. FOCA: a new quality-controlled database of floods and catchment descriptors in Italy P. Claps et al. 10.5194/essd-16-1503-2024
2. CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India N. Mangukiya et al. 10.5194/essd-17-461-2025
3. NAC2H: The North American Climate Change and Hydroclimatology Data Set R. Arsenault et al. 10.1029/2020WR027097
4. The Abuse of Popular Performance Metrics in Hydrologic Modeling M. Clark et al. 10.1029/2020WR029001
5. Leveraging ensemble meteorological forcing data to improve parameter estimation of hydrologic models H. Liu et al. 10.1002/hyp.14410
6. The Impact of Meteorological Forcing Uncertainty on Hydrological Modeling: A Global Analysis of Cryosphere Basins G. Tang et al. 10.1029/2022WR033767
7. Deep Learning for Streamflow Regionalization for Ungauged Basins: Application of Long-Short-Term-Memory Cells in Semiarid Regions F. Nogueira Filho et al. 10.3390/w14091318
8. A hydrologic signature approach to analysing wildfire impacts on overland flow L. Bolotin & H. McMillan 10.1002/hyp.15215
9. Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration Y. Khoshkalam et al. 10.1016/j.jhydrol.2023.129682
10. Physics-aware machine learning revolutionizes scientific paradigm for process-based modeling in hydrology Q. Xu et al. 10.1016/j.earscirev.2025.105276
11. From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling W. Tsai et al. 10.1038/s41467-021-26107-z
12. Design flood estimation using extreme Gradient Boosting-based on Bayesian optimization D. Jarajapu et al. 10.1016/j.jhydrol.2022.128341
13. Transfer learning using the global Caravan dataset for developing a local river streamflow prediction model A. Alzhanov et al. 10.1016/j.envsoft.2025.106691
14. A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments P. Yeste et al. 10.5194/hess-28-5331-2024
15. A decomposition approach to evaluating the local performance of global streamflow reanalysis T. Zhao et al. 10.5194/hess-28-3597-2024
16. Improved Regionalization of the CN Method for Extreme Events at Ungauged Sites across the US T. Neelam et al. 10.1061/JHYEFF.HEENG-6180
17. A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) X. Chen et al. 10.5194/essd-15-4463-2023
18. The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset C. Alvarez-Garreton et al. 10.5194/hess-22-5817-2018
19. Impact of spatial distribution information of rainfall in runoff simulation using deep learning method Y. Wang & H. Karimi 10.5194/hess-26-2387-2022
20. Evaluating stochastic rainfall models for hydrological modelling T. Nguyen et al. 10.1016/j.jhydrol.2023.130381
21. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain G. Coxon et al. 10.5194/essd-12-2459-2020
22. Causal relationships of vegetation productivity with root zone water availability and atmospheric dryness at the catchment scale G. Abeshu et al. 10.5194/hess-29-1847-2025
23. Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data K. Tayal et al. 10.1088/1748-9326/ad6fb7
24. 无资料流域日径流预测<bold>:</bold> 模型迁移优化及参考流域决策分析 昕. 宋 et al. 10.1360/SSTe-2024-0299
25. A dataset of lake-catchment characteristics for the Tibetan Plateau J. Liu et al. 10.5194/essd-14-3791-2022
26. Location, location, location – Considering relative catchment location to understand subsurface losses M. Kiraz-Safari et al. 10.1016/j.jhydrol.2024.132328
27. CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany R. Loritz et al. 10.5194/essd-16-5625-2024
28. Time to Update the Split‐Sample Approach in Hydrological Model Calibration H. Shen et al. 10.1029/2021WR031523
29. A differentiable, physics-based hydrological model and its evaluation for data-limited basins W. Ouyang et al. 10.1016/j.jhydrol.2024.132471
30. Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what is the best way to leverage regionalised information? S. Chidepudi et al. 10.5194/hess-29-841-2025
31. Advancing traditional strategies for testing hydrological model fitness in a changing climate A. Todorović et al. 10.1080/02626667.2022.2104646
32. Train, Inform, Borrow, or Combine? Approaches to Process‐Guided Deep Learning for Groundwater‐Influenced Stream Temperature Prediction J. Barclay et al. 10.1029/2023WR035327
33. How is Baseflow Index (BFI) impacted by water resource management practices? J. Bloomfield et al. 10.5194/hess-25-5355-2021
34. Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations W. Knoben et al. 10.5194/gmd-12-2463-2019
35. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132269
36. Rising temperatures intensify drought propagation and severity across the contiguous United States H. Yan et al. 10.1038/s44304-025-00134-y
37. A watershed-scale rainfall infiltration model incorporating water and energy constraints B. Yan & J. Chang 10.1016/j.jhydrol.2025.133561
38. Future streamflow regime changes in the United States: assessment using functional classification M. Brunner et al. 10.5194/hess-24-3951-2020
39. Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E) J. Mai et al. 10.1061/(ASCE)HE.1943-5584.0002097
40. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
41. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132003
42. The persistence of snow on the ground affects the shape of streamflow hydrographs over space and time: a continental-scale analysis E. Le et al. 10.3389/fenvs.2023.1207508
43. Dive into transfer-learning for daily rainfall-runoff modeling in data-limited basins W. Ouyang et al. 10.1016/j.jhydrol.2025.133063
44. Daily runoff prediction for ungauged basins: Optimization of model transfer and selection of donor basins X. Song et al. 10.1007/s11430-024-1630-7
45. Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis 10.5194/adgeo-45-201-2018
46. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
47. Streamflow-based evaluation of climate model sub-selection methods J. Kiesel et al. 10.1007/s10584-020-02854-8
48. Many Commonly Used Rainfall‐Runoff Models Lack Long, Slow Dynamics: Implications for Runoff Projections K. Fowler et al. 10.1029/2019WR025286
49. Regionalization for Ungauged Catchments — Lessons Learned From a Comparative Large‐Sample Study S. Pool et al. 10.1029/2021WR030437
50. Runoff for Russia (RFR v1.0): The Large-Sample Dataset of Simulated Runoff and Its Characteristics G. Ayzel 10.3390/data8020031
51. Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets F. Hasan et al. 10.3390/w16131904
52. Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US G. Konapala et al. 10.1088/1748-9326/aba927
53. Correspondence Between Model Structures and Hydrological Signatures: A Large‐Sample Case Study Using 508 Brazilian Catchments P. David et al. 10.1029/2021WR030619
54. QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. 10.5194/essd-14-3715-2022
55. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil V. Chagas et al. 10.5194/essd-12-2075-2020
56. Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
57. Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. 10.5194/hess-25-5517-2021
58. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
59. Generating interpretable rainfall-runoff models automatically from data T. Dantzer & B. Kerkez 10.1016/j.advwatres.2024.104796
60. Modeling hydraulic heads with impulse response functions in different environmental settings of the Baltic countries M. Jemeļjanova et al. 10.1016/j.ejrh.2023.101416
61. Toward interpretable LSTM-based modeling of hydrological systems L. De la Fuente et al. 10.5194/hess-28-945-2024
62. Investigating the streamflow simulation capability of a new mass-conserving long short-term memory (MC-LSTM) model across the contiguous United States Y. Wang et al. 10.1016/j.jhydrol.2025.133161
63. Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023–2032 T. van Hateren et al. 10.1080/02626667.2023.2170754
64. Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction T. Xie et al. 10.3390/w16010069
65. A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Z. Yin et al. 10.5194/essd-16-1559-2024
66. DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States P. Ghaneei & H. Moradkhani 10.1038/s41597-025-04389-y
67. VMDI-LSTM-ED: A novel enhanced decomposition ensemble model incorporating data integration for accurate non-stationary daily streamflow forecasting J. Liu et al. 10.1016/j.jhydrol.2025.132769
68. Comparison of eight filter-based feature selection methods for monthly streamflow forecasting – Three case studies on CAMELS data sets K. Ren et al. 10.1016/j.jhydrol.2020.124897
69. Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network C. Frank et al. 10.3389/frwa.2023.1126310
70. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar 10.5194/hess-26-2019-2022
71. Runoff Prediction Based on Deep Residual Shrinkage Long Short-term Memory Network Z. Guan et al. 10.1088/1742-6596/2400/1/012016
72. Can transfer learning improve hydrological predictions in the alpine regions? Y. Yao et al. 10.1016/j.jhydrol.2023.130038
73. Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions A. Gupta & S. McKenna 10.1016/j.hydroa.2024.100198
74. Catchment processes can amplify the effect of increasing rainfall variability M. Müller et al. 10.1088/1748-9326/ac153e
75. Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction C. Deng et al. 10.1016/j.jenvman.2024.121299
76. On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. 10.5194/hess-28-5011-2024
77. Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs Y. Shen et al. 10.5194/essd-15-2781-2023
78. Dissolving the mystery of subsurface controls on snowmelt–discharge dynamics in karst mountain watersheds using hydrologic timeseries D. Thurber et al. 10.1002/hyp.15170
79. Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins Y. Sawada et al. 10.3178/hrl.16.73
80. Hydrologic Sensitivities and ENSO Variability Across Hydrological Regimes in Central Chile (28°–41°S) D. Hernandez et al. 10.1029/2021WR031860
81. LakeBeD-US: a benchmark dataset for lake water quality time series and vertical profiles B. McAfee et al. 10.5194/essd-17-3141-2025
82. Learning landscape features from streamflow with autoencoders A. Bassi et al. 10.5194/hess-28-4971-2024
83. A unified framework of water balance models for monthly, annual, and mean annual timescales X. Zhang et al. 10.1016/j.jhydrol.2020.125186
84. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
85. Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization S. Ragettli et al. 10.1016/j.jhydrol.2017.10.031
86. The proper care and feeding of CAMELS: How limited training data affects streamflow prediction M. Gauch et al. 10.1016/j.envsoft.2020.104926
87. Multi-step ahead forecasting of daily streamflow based on the transform-based deep learning model under different scenarios M. He et al. 10.1038/s41598-025-89837-w
88. A new criteria for determining the best decomposition level and filter for wavelet-based data-driven forecasting frameworks- validating using three case studies on the CAMELS dataset M. Mazarei Behbahani & A. Mazarei 10.1007/s00477-023-02531-z
89. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. 10.1016/j.jhydrol.2023.129160
90. National-scale geodatabase of catchment characteristics in the Philippines for river management applications R. Boothroyd et al. 10.1371/journal.pone.0281933
91. TOSSH: A Toolbox for Streamflow Signatures in Hydrology S. Gnann et al. 10.1016/j.envsoft.2021.104983
92. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships K. Xie et al. 10.1016/j.jhydrol.2021.127043
93. Quantifying the Geomorphic Effect of Floods Using Satellite Observations of River Mobility A. Leenman et al. 10.1029/2023GL103875
94. Variational Bayesian dropout with a Gaussian prior for recurrent neural networks application in rainfall–runoff modeling S. Sadeghi Tabas & S. Samadi 10.1088/1748-9326/ac7247
95. What is the hydrologically effective area of a catchment? Y. Liu et al. 10.1088/1748-9326/aba7e5
96. The role of previously glaciated landscapes in spatiotemporal variability of streamflow in snow-dominated watersheds: British Columbia, Canada X. Chen et al. 10.1016/j.ejrh.2023.101478
97. Horton Index: Conceptual Framework for Exploring Multi‐Scale Links Between Catchment Water Balance and Vegetation Dynamics G. Abeshu & H. Li 10.1029/2020WR029343
98. Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions D. Klotz et al. 10.5194/hess-28-3665-2024
99. Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems U. Ehret & P. Dey 10.5194/hess-27-2591-2023
100. AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods A. Abbas et al. 10.5194/gmd-15-3021-2022
101. Machine‐Learning Reveals Equifinality in Drivers of Stream DOC Concentration at Continental Scales K. Underwood et al. 10.1029/2021WR030551
102. Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta 10.1016/j.ejrh.2023.101607
103. Climate Controls on River Chemistry L. Li et al. 10.1029/2021EF002603
104. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach B. Li et al. 10.1016/j.jhydrol.2023.130107
105. The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: A case study for six basins from the CAMELS dataset G. Ayzel & M. Heistermann 10.1016/j.cageo.2021.104708
106. Modeling streamflow variability at the regional scale: (1) perceptual model development through signature analysis F. Fenicia & J. McDonnell 10.1016/j.jhydrol.2021.127287
107. Crossing the rural–urban boundary in hydrological modelling: How do conceptual rainfall–runoff models handle the specificities of urbanized catchments? M. Saadi et al. 10.1002/hyp.13808
108. A step toward global-scale applicability and transferability of flow duration curve studies: A flow duration curve review (2000–2020) C. Leong & Y. Yokoo 10.1016/j.jhydrol.2021.126984
109. How Do Climate and Catchment Attributes Influence Flood Generating Processes? A Large‐Sample Study for 671 Catchments Across the Contiguous USA L. Stein et al. 10.1029/2020WR028300
110. Novel Parameter Calibration Method of WRF-Hydro in Ungauged Areas Combining Satellite-Based Soil Moisture and Multisource Meteorological Data Q. Zhao et al. 10.1007/s11269-025-04336-x
111. Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA T. Barnhart et al. 10.1002/rra.4028
112. On the use of distribution-adaptive likelihood functions: Generalized and universal likelihood functions, scoring rules and multi-criteria ranking J. Vrugt et al. 10.1016/j.jhydrol.2022.128542
113. Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes H. McMillan et al. 10.1029/2021WR031751
114. What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach K. Li & S. Razavi 10.1016/j.jhydrol.2024.131835
115. Climate shapes baseflows, influencing drought severity M. Zaerpour et al. 10.1088/1748-9326/ad975a
116. A Multi-Task Learning Based Runoff Forecasting Model for Multi-Scale Chaotic Hydrological Time Series H. Zuo et al. 10.1007/s11269-023-03681-z
117. A Global-Scale Investigation of Stochastic Similarities in Marginal Distribution and Dependence Structure of Key Hydrological-Cycle Processes P. Dimitriadis et al. 10.3390/hydrology8020059
118. Regionalization in a Global Hydrologic Deep Learning Model: From Physical Descriptors to Random Vectors X. Li et al. 10.1029/2021WR031794
119. Development of the consider cubature Kalman filter for state estimation of hydrological models with parameter uncertainty Y. Sun et al. 10.1016/j.jhydrol.2023.130080
120. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets F. Kratzert et al. 10.5194/hess-23-5089-2019
121. Hydrological post-processing for predicting extreme quantiles H. Tyralis & G. Papacharalampous 10.1016/j.jhydrol.2023.129082
122. Soil Water Accounting Network (SWAN): a novel neural network for modeling conceptual hydrological processes Z. Fang et al. 10.1016/j.jhydrol.2025.133562
123. HESS Opinions: Towards a common vision for the future of hydrological observatories P. Nasta et al. 10.5194/hess-29-465-2025
124. Malá zdrojová povodí – jejich prostorové vymezení a klasifikace z hlediska rizika ohrožení rychlým odtokem P. Kavka et al. 10.46555/VTEI.2022.11.001
125. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
126. Seasonal patterns and hydrological regulations of root zone storage capacity across United States S. Du et al. 10.1016/j.agrformet.2025.110428
127. Pitfalls and a feasible solution for using KGE as an informal likelihood function in MCMC methods: DREAM(ZS) as an example Y. Liu et al. 10.5194/hess-26-5341-2022
128. Investigating the potential of EMA-embedded feature selection method for ESVR and LSTM to enhance the robustness of monthly streamflow forecasting from local meteorological information L. Xu et al. 10.1016/j.jhydrol.2024.131230
129. Upland Hillslope Groundwater Subsidy Affects Low‐Flow Storage–Discharge Relationship H. Li & A. Ameli 10.1029/2022WR034155
130. Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
131. Hydrological signatures describing the translation of climate seasonality into streamflow seasonality S. Gnann et al. 10.5194/hess-24-561-2020
132. Retracted: Spatiotemporal convolutional long short-term memory for regional streamflow predictions A. Mohammed & G. Corzo 10.1016/j.jenvman.2023.119585
133. Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés 10.1016/j.jhydrol.2024.132569
134. The Bellinge data set: open data and models for community-wide urban drainage systems research A. Nedergaard Pedersen et al. 10.5194/essd-13-4779-2021
135. Improving deep learning-based streamflow forecasting under trend varying conditions through evaluation of new wavelet preprocessing technique M. Behbahani et al. 10.1007/s00477-024-02788-y
136. Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach M. Brunner & E. Gilleland 10.5194/hess-24-3967-2020
137. Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms H. Tyralis et al. 10.1007/s00521-020-05172-3
138. Deep learning in water protection of resources, environment, and ecology: achievement and challenges X. Fu et al. 10.1007/s11356-024-31963-5
139. Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework T. Botterill & H. McMillan 10.1029/2022WR033091
140. Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data S. Gharari et al. 10.1029/2020WR027948
141. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al. 10.5194/essd-13-3847-2021
142. What is the near-natural catchment? An application of hydrological signatures assessment H. Xu et al. 10.1016/j.ecolind.2025.113209
143. Derivation and analysis of spatial variability in master recession curves with mixed effects model B. Chen et al. 10.1016/j.jhydrol.2025.133853
144. Does grouping watersheds by hydrographic regions offer any advantages in fine-tuning transfer learning model for temporal and spatial streamflow predictions? Y. Khoshkalam et al. 10.1016/j.jhydrol.2024.132540
145. CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. 10.5194/essd-17-1551-2025
146. Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis E. Moges et al. 10.3390/w13010028
147. Assimilation of remotely sensed evapotranspiration products for streamflow simulation based on the CAMELS data sets C. Deng et al. 10.1016/j.jhydrol.2023.130574
148. Assessing the performance and robustness of two conceptual rainfall-runoff models on a worldwide sample of watersheds T. Mathevet et al. 10.1016/j.jhydrol.2020.124698
149. Effects of Multi-Step-Ahead Prediction Strategies on LSTM-Based Runoff Prediction J. Jiang et al. 10.1007/s11269-025-04332-1
150. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks F. Kratzert et al. 10.5194/hess-22-6005-2018
151. Hyper-resolution PCR-GLOBWB: opportunities and challenges from refining model spatial resolution to 1 km over the European continent J. Hoch et al. 10.5194/hess-27-1383-2023
152. Taking full advantage of ‘low-quality’ datasets in watershed modeling and management: From a perspective of parameter calibration Y. Rong et al. 10.1016/j.jenvman.2023.119955
153. A process-driven deep learning hydrological model for daily rainfall-runoff simulation H. Li et al. 10.1016/j.jhydrol.2024.131434
154. Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5) H. Yan et al. 10.1029/2022MS003312
155. CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland M. Höge et al. 10.5194/essd-15-5755-2023
156. Snow monitoring at strategic locations improves water supply forecasting more than basin-wide mapping M. Raleigh et al. 10.1038/s43247-025-02660-z
157. A statistical approach for identifying factors governing streamflow recession behaviour H. Li & A. Ameli 10.1002/hyp.14718
158. An Aridity Index‐Based Formulation of Streamflow Components A. Meira Neto et al. 10.1029/2020WR027123
159. Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States H. Cai et al. 10.1016/j.ejrh.2021.100930
160. A Quantitative Hydrological Climate Classification Evaluated With Independent Streamflow Data W. Knoben et al. 10.1029/2018WR022913
161. To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al. 10.5194/hess-28-2705-2024
162. Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise W. Knoben & D. Spieler 10.5194/hess-26-3299-2022
163. The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata H. Do et al. 10.5194/essd-10-765-2018
164. Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability L. Trotter et al. 10.5194/gmd-15-6359-2022
165. PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al. 10.3390/w13081048
166. A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis 10.3389/frwa.2022.961954
167. Enhancing Hydro-climatic and land parameter forecasting using Transformer networks S. Subhadarsini et al. 10.1016/j.jhydrol.2025.132906
168. Leveraging gauge networks and strategic discharge measurements to aid the development of continuous streamflow records M. Vlah et al. 10.5194/hess-28-545-2024
169. CABra: a novel large-sample dataset for Brazilian catchments A. Almagro et al. 10.5194/hess-25-3105-2021
170. Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset A. Tounsi et al. 10.1007/s00521-023-08922-1
171. Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma 10.1016/j.jhydrol.2024.130635
172. How Probable Is Widespread Flooding in the United States? M. Brunner et al. 10.1029/2020WR028096
173. Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters L. Bouaziz et al. 10.5194/hess-26-1295-2022
174. Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges E. Ragno et al. 10.5194/hess-26-1695-2022
175. Why does snowmelt-driven streamflow response to warming vary? A data-driven review and predictive framework B. Gordon et al. 10.1088/1748-9326/ac64b4
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362. Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al. 10.5194/hess-26-5493-2022
363. Rainfall-runoff modeling using long short-term memory based step-sequence framework H. Yin et al. 10.1016/j.jhydrol.2022.127901
364. Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment M. Dal Molin et al. 10.5194/hess-24-1319-2020
365. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges N. Addor et al. 10.1080/02626667.2019.1683182
366. Getting your money's worth: Testing the value of data for hydrological model calibration J. Seibert et al. 10.1002/hyp.15094
367. Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning S. Kim et al. 10.1038/s41597-024-03959-w
368. Vegetation optimality explains the convergence of catchments on the Budyko curve R. Nijzink & S. Schymanski 10.5194/hess-26-6289-2022
369. When good signatures go bad: Applying hydrologic signatures in large sample studies H. McMillan et al. 10.1002/hyp.14987
370. An autoencoder-based snow drought index S. Rasiya Koya et al. 10.1038/s41598-023-47999-5
371. Large-sample hydrology – a few camels or a whole caravan? F. Clerc-Schwarzenbach et al. 10.5194/hess-28-4219-2024
372. Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores W. Knoben et al. 10.5194/hess-23-4323-2019
373. Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. 10.5194/hess-28-2505-2024
374. Exploring Signature‐Based Model Calibration for Streamflow Prediction in Ungauged Basins M. Dal Molin et al. 10.1029/2022WR031929
375. Control of climate and physiography on runoff response behavior through use of catchment classification and machine learning S. Du et al. 10.1016/j.scitotenv.2023.166422
376. Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al. 10.5194/hess-29-1277-2025
377. On the value of a history of hydrology and the establishment of a History of Hydrology Working Group K. Beven et al. 10.1080/02626667.2025.2452357
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379. Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning Y. Fang et al. 10.3390/rs14184609
380. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
381. CAMELS-AUS v2: updated hydrometeorological time series and landscape attributes for an enlarged set of catchments in Australia K. Fowler et al. 10.5194/essd-17-4079-2025
382. GRQA: Global River Water Quality Archive H. Virro et al. 10.5194/essd-13-5483-2021
383. Time‐Varying Sensitivity Analysis Reveals Relationships Between Watershed Climate and Variations in Annual Parameter Importance in Regions With Strong Interannual Variability R. Basijokaite & C. Kelleher 10.1029/2020WR028544
384. Decreases in Mean Annual Streamflow and Interannual Streamflow Variability Across Snow‐Affected Catchments Under a Warming Climate Z. Liu et al. 10.1029/2021GL097442
385. Multi‐Task Deep Learning of Daily Streamflow and Water Temperature J. Sadler et al. 10.1029/2021WR030138
386. China’s nationwide streamflow decline driven by landscape changes and human interventions K. Wang et al. 10.1126/sciadv.adu8032
387. Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama H. Gholizadeh et al. 10.1016/j.scitotenv.2023.165884
388. Functional data analysis to investigate controls on and changes in the seasonality of UK baseflow K. Leeming et al. 10.1080/02626667.2024.2434714
389. A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan 10.5194/hess-29-785-2025
390. Technical note: How many models do we need to simulate hydrologic processes across large geographical domains? W. Knoben et al. 10.5194/hess-29-2361-2025
391. Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model J. Aerts et al. 10.5194/hess-26-4407-2022
392. Streamflow regime-based classification and hydrologic similarity analysis of catchment behavior using differentiable modeling with multiphysics outputs Y. Hu et al. 10.1016/j.jhydrol.2025.132766
393. Comparison of flood simulation capabilities of a hydrologic model and a machine learning model L. Liu et al. 10.1002/joc.7738
394. CAMELS-Chem: augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data G. Sterle et al. 10.5194/hess-28-611-2024
395. Meteorological and hydrological dry-to-wet transition events are only weakly related over European catchments M. Brunner et al. 10.1088/1748-9326/ade72c
396. A conceptual model framework for integrating monitored-unmonitored and surface-subsurface flow contributions to the Baltic Sea A. Vijayan et al. 10.3389/feart.2025.1601966
397. Direct and Indirect Drivers of Energy and Nutrient Availability in Freshwater Ecosystems Across Spatial Scales H. Fazekas et al. 10.1029/2022GB007580
398. Continental-scale streamflow modeling of basins with reservoirs: Towards a coherent deep-learning-based strategy W. Ouyang et al. 10.1016/j.jhydrol.2021.126455
399. A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al. 10.1016/j.jhydrol.2023.129269
400. Assessment of Future Risks of Seasonal Municipal Water Shortages Across North America J. Janssen et al. 10.3389/feart.2021.730631
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402. Regional Patterns and Physical Controls of Streamflow Generation Across the Conterminous United States S. Wu et al. 10.1029/2020WR028086
403. Hydrological characteristics of Australia: national catchment classification and regional relationships J. Jaffrés et al. 10.1016/j.jhydrol.2022.127969
404. On the choice of calibration metrics for “high-flow” estimation using hydrologic models N. Mizukami et al. 10.5194/hess-23-2601-2019
405. Advancing the science of headwater streamflow for global water protection H. Golden et al. 10.1038/s44221-024-00351-1
406. Runoff predictions in ungauged basins using sequence-to-sequence models H. Yin et al. 10.1016/j.jhydrol.2021.126975
407. Hydrologic Model Sensitivity to Temporal Aggregation of Meteorological Forcing Data: A Case Study for the Contiguous United States A. Van Beusekom et al. 10.1175/JHM-D-21-0111.1
408. Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States H. Yan et al. 10.1038/s41597-023-02049-7
409. Strength and Memory of Precipitation's Control Over Streamflow Across the Conterminous United States E. Moges et al. 10.1029/2021WR030186
410. Terrestrial ecosystems enhanced root zone water storage capacity in response to climate change over the past four decades Q. Xi et al. 10.1016/j.scib.2025.06.027
411. In Quest of Calibration Density and Consistency in Hydrologic Modeling: Distributed Parameter Calibration against Streamflow Characteristics Y. Yang et al. 10.1029/2018WR024178
412. Advances in Quantifying Streamflow Variability Across Continental Scales: 1. Identifying Natural and Anthropogenic Controlling Factors in the USA Using a Spatially Explicit Modeling Method R. Alexander et al. 10.1029/2019WR025001
413. Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al. 10.1016/j.jhydrol.2019.123957
414. A data-centric perspective on the information needed for hydrological uncertainty predictions A. Auer et al. 10.5194/hess-28-4099-2024
415. Streamflow prediction in ungauged catchments through use of catchment classification and deep learning M. He et al. 10.1016/j.jhydrol.2024.131638
416. Comparing three machine learning algorithms with existing methods for natural streamflow estimation S. Mehrvand et al. 10.1080/02626667.2023.2273402
417. Combining global precipitation data and machine learning to predict flood peaks in ungauged areas with similar climate Z. Rasheed et al. 10.1016/j.advwatres.2024.104781
418. A water-energy complementary model for monthly runoff simulation Y. Zou et al. 10.1016/j.jhydrol.2024.131624
419. How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset H. Tyralis et al. 10.1016/j.jhydrol.2019.04.070
420. Earthquake Nowcasting with Deep Learning G. Fox et al. 10.3390/geohazards3020011
421. Quantile-Based Hydrological Modelling H. Tyralis & G. Papacharalampous 10.3390/w13233420
422. Changes in streamflow statistical structure across the United States due to recent climate change A. Gupta et al. 10.1016/j.jhydrol.2023.129474
423. Is There a Baseflow Budyko Curve? S. Gnann et al. 10.1029/2018WR024464
424. EStreams: An integrated dataset and catalogue of streamflow, hydro-climatic and landscape variables for Europe T. do Nascimento et al. 10.1038/s41597-024-03706-1
425. Accelerating flood warnings by 10 hours: the power of river network topology in AI-enhanced flood forecasting H. Wang et al. 10.1038/s44304-025-00083-6
426. Factors influencing calibration of a semi-distributed mixed runoff hydrological model: A study on nine small mountain catchments in China L. Wen et al. 10.1016/j.ejrh.2023.101418
427. Expectile-based hydrological modelling for uncertainty estimation: Life after mean H. Tyralis et al. 10.1016/j.jhydrol.2022.128986
428. Investigate the rainfall-runoff relationship and hydrological concepts inside LSTM Y. Hu et al. 10.1016/j.envsoft.2025.106527
429. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. 10.1016/j.jhydrol.2022.127781
430. HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool E. Moges et al. 10.3389/feart.2022.884766
431. Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti R. Bathelemy et al. 10.5194/essd-16-2073-2024
432. HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al. 10.5194/hess-28-4187-2024
433. Comparative analysis of machine learning based dissolved oxygen predictions in the Yellow River Basin: The role of diverse environmental predictors L. Liu et al. 10.1016/j.jenvman.2025.127138
434. Inference of Parameters for a Global Hydrological Model: Identifiability and Predictive Uncertainties of Climate‐Based Parameters T. Yoshida et al. 10.1029/2021WR030660
435. Empowering Regional Rainfall-Runoff Modeling Through Encoder–Decoder Based on Convolutional Neural Networks W. Jiang et al. 10.3390/w17030339
436. Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling K. Li et al. 10.5194/hess-25-4947-2021
437. Enhancing prediction and inference of daily in-stream nutrient and sediment concentrations using an extreme gradient boosting based water quality estimation tool - XGBest S. Jain et al. 10.1016/j.scitotenv.2025.178517
438. Findings from a National Survey of Canadian perspectives on predicting river channel migration and river bank erosion C. Kupferschmidt & A. Binns 10.1002/rra.4336
439. Data‐Driven Worldwide Quantification of Large‐Scale Hydroclimatic Covariation Patterns and Comparison With Reanalysis and Earth System Modeling N. Ghajarnia et al. 10.1029/2020WR029377
440. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
441. Climate change impacts model parameter sensitivity – implications for calibration strategy and model diagnostic evaluation L. Melsen & B. Guse 10.5194/hess-25-1307-2021
442. Mapping (dis)agreement in hydrologic projections L. Melsen et al. 10.5194/hess-22-1775-2018
443. Advancing streamflow prediction in data-scarce regions through vegetation-constrained distributed hybrid ecohydrological models L. Zhong et al. 10.1016/j.jhydrol.2024.132165
444. Setting expectations for hydrologic model performance with an ensemble of simple benchmarks W. Knoben 10.1002/hyp.15288
445. When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling Y. Song et al. 10.5194/hess-28-3051-2024
446. On the choice of calibration metrics for “high-flow” estimation using hydrologic models N. Mizukami et al. 10.5194/hess-23-2601-2019
447. Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores W. Knoben et al. 10.5194/hess-23-4323-2019
448. Technical note: Diagnostic efficiency – specific evaluation of model performance R. Schwemmle et al. 10.5194/hess-25-2187-2021
449. Swiss data quality: augmenting CAMELS-CH with isotopes, water quality, agricultural and atmospheric data T. do Nascimento et al. 10.1038/s41597-025-05625-1
450. To What Extent Are Changes in Flood Magnitude Related to Changes in Precipitation Extremes? H. Do et al. 10.1029/2020GL088684
451. BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology J. Senent-Aparicio et al. 10.1038/s41597-024-03594-5
452. Hydrological Interpretation of a Statistical Measure of Basin Complexity S. Pande & M. Moayeri 10.1029/2018WR022675
453. New projections of 21st century climate and hydrology for Alaska and Hawaiʻi N. Mizukami et al. 10.1016/j.cliser.2022.100312